Relational Data Mining Through Extraction of Representative Exemplars

With the growing interest on Network Analysis, Relational Data Mining is becoming an emphasized domain of Data Mining. This paper addresses the problem of extracting representative elements from a relational dataset. After defining the notion of degree of representativeness, computed using the Borda aggregation procedure, we present the extraction of exemplars which are the representative elements of the dataset. We use these concepts to build a network on the dataset. We expose the main properties of these notions and we propose two typical applications of our framework. The first application consists in resuming and structuring a set of binary images and the second in mining co-authoring relation in a research team.

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Source https://hal.science/hal-00701979
Author Blanchard, Frédéric, Herbin, Michel
Maintainer CCSD
Last Updated May 16, 2026, 04:29 (UTC)
Created May 16, 2026, 04:29 (UTC)
Identifier hal-00701979
Language en
Rights https://about.hal.science/hal-authorisation-v1/
contributor Centre de Recherche en Sciences et Technologies de l'Information et de la Communication - EA 3804 (CRESTIC) ; Université de Reims Champagne-Ardenne (URCA)
creator Blanchard, Frédéric
date 2012-06-04T00:00:00
harvest_object_id a9fd2984-0345-4405-a991-c3383eb0110c
harvest_source_id 3374d638-d20b-4672-ba96-a23232d55657
harvest_source_title test moissonnage SELUNE
metadata_modified 2025-02-26T00:00:00
set_spec type:UNDEFINED